Overview

Brought to you by YData

Dataset statistics

Number of variables41
Number of observations19357
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.1 MiB
Average record size in memory328.0 B

Variable types

Numeric16
Categorical25

Alerts

Deuda_Pendiente is highly overall correlated with Mezcla_Crediticia_Bad and 1 other fieldsHigh correlation
Edad is highly overall correlated with Edad_Historial_Credito and 1 other fieldsHigh correlation
Edad_Historial_Credito is highly overall correlated with EdadHigh correlation
Inversion_Mensual is highly overall correlated with Salario_Mensual and 2 other fieldsHigh correlation
Mezcla_Crediticia_Bad is highly overall correlated with Deuda_Pendiente and 5 other fieldsHigh correlation
Mezcla_Crediticia_Good is highly overall correlated with Mezcla_Crediticia_Standard and 4 other fieldsHigh correlation
Mezcla_Crediticia_Standard is highly overall correlated with Mezcla_Crediticia_Good and 2 other fieldsHigh correlation
Num_Consultas_Credito is highly overall correlated with Mezcla_Crediticia_BadHigh correlation
Num_Pagos_Retrasados is highly overall correlated with Mezcla_Crediticia_Bad and 2 other fieldsHigh correlation
Num_Tarjetas_Credito is highly overall correlated with Mezcla_Crediticia_BadHigh correlation
Pago_Minimo_No is highly overall correlated with Mezcla_Crediticia_Good and 1 other fieldsHigh correlation
Pago_Minimo_Yes is highly overall correlated with Mezcla_Crediticia_Good and 1 other fieldsHigh correlation
Retraso_Pago is highly overall correlated with Mezcla_Crediticia_BadHigh correlation
Salario_Mensual is highly overall correlated with Inversion_Mensual and 3 other fieldsHigh correlation
Saldo_Mensual is highly overall correlated with Salario_MensualHigh correlation
Tasa_Interes is highly overall correlated with Mezcla_Crediticia_Bad and 2 other fieldsHigh correlation
Total_Cuota_Mensual is highly overall correlated with Inversion_Mensual and 2 other fieldsHigh correlation
credit_history_ratio is highly overall correlated with EdadHigh correlation
debt_to_income is highly overall correlated with Deuda_Pendiente and 2 other fieldsHigh correlation
payment_to_income is highly overall correlated with Total_Cuota_MensualHigh correlation
Comportamiento_de_Pago_Low_spent_Large_value_payments is highly imbalanced (50.2%) Imbalance
Mezcla_Crediticia_Bad is highly imbalanced (67.5%) Imbalance
Ocupacion_Architect is highly imbalanced (59.8%) Imbalance
Ocupacion_Developer is highly imbalanced (66.5%) Imbalance
Ocupacion_Doctor is highly imbalanced (64.2%) Imbalance
Ocupacion_Engineer is highly imbalanced (63.8%) Imbalance
Ocupacion_Entrepreneur is highly imbalanced (64.5%) Imbalance
Ocupacion_Journalist is highly imbalanced (67.4%) Imbalance
Ocupacion_Lawyer is highly imbalanced (64.2%) Imbalance
Ocupacion_Manager is highly imbalanced (63.3%) Imbalance
Ocupacion_Mechanic is highly imbalanced (64.9%) Imbalance
Ocupacion_Media_Manager is highly imbalanced (63.8%) Imbalance
Ocupacion_Musician is highly imbalanced (65.8%) Imbalance
Ocupacion_Scientist is highly imbalanced (64.1%) Imbalance
Ocupacion_Teacher is highly imbalanced (65.6%) Imbalance
Ocupacion_Writer is highly imbalanced (65.4%) Imbalance
Retraso_Pago has 330 (1.7%) zeros Zeros
Num_Pagos_Retrasados has 519 (2.7%) zeros Zeros
Num_Consultas_Credito has 2310 (11.9%) zeros Zeros
Inversion_Mensual has 196 (1.0%) zeros Zeros

Reproduction

Analysis started2025-04-17 04:52:32.928522
Analysis finished2025-04-17 04:53:00.978908
Duration28.05 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Edad
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.630676
Minimum18
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:01.038496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile18
Q122
median26
Q333
95-th percentile42
Maximum50
Range32
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.4740396
Coefficient of variation (CV)0.27049789
Kurtosis-0.29458367
Mean27.630676
Median Absolute Deviation (MAD)5
Skewness0.74530227
Sum534847
Variance55.861268
MonotonicityNot monotonic
2025-04-17T00:53:01.146240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
19 1291
 
6.7%
20 1209
 
6.2%
22 1202
 
6.2%
21 1167
 
6.0%
24 1156
 
6.0%
23 1128
 
5.8%
18 1111
 
5.7%
25 1039
 
5.4%
26 971
 
5.0%
27 888
 
4.6%
Other values (23) 8195
42.3%
ValueCountFrequency (%)
18 1111
5.7%
19 1291
6.7%
20 1209
6.2%
21 1167
6.0%
22 1202
6.2%
23 1128
5.8%
24 1156
6.0%
25 1039
5.4%
26 971
5.0%
27 888
4.6%
ValueCountFrequency (%)
50 9
 
< 0.1%
49 56
 
0.3%
48 94
 
0.5%
47 65
 
0.3%
46 108
 
0.6%
45 172
0.9%
44 222
1.1%
43 203
1.0%
42 176
0.9%
41 315
1.6%

Salario_Mensual
Real number (ℝ)

High correlation 

Distinct3322
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4871.9399
Minimum332.12833
Maximum15115.19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:01.253257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum332.12833
5-th percentile1012.2096
Q11985.7408
median3529.8733
Q37133.2933
95-th percentile11748.077
Maximum15115.19
Range14783.062
Interquartile range (IQR)5147.5525

Descriptive statistics

Standard deviation3510.8863
Coefficient of variation (CV)0.72063416
Kurtosis-0.10252891
Mean4871.9399
Median Absolute Deviation (MAD)2052.0767
Skewness0.90698016
Sum94306141
Variance12326323
MonotonicityNot monotonic
2025-04-17T00:53:01.368010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1315.560833 14
 
0.1%
6769.13 12
 
0.1%
5766.491667 10
 
0.1%
3953.978333 8
 
< 0.1%
4697.666667 8
 
< 0.1%
5807.178333 8
 
< 0.1%
961.1533333 8
 
< 0.1%
5868.18 8
 
< 0.1%
1572.2625 8
 
< 0.1%
11104.69333 8
 
< 0.1%
Other values (3312) 19265
99.5%
ValueCountFrequency (%)
332.1283333 5
< 0.1%
368.3741667 2
 
< 0.1%
396.3441667 7
< 0.1%
403.2541667 6
< 0.1%
406.5733333 7
< 0.1%
428.9241667 4
< 0.1%
431.73625 7
< 0.1%
434.6083333 7
< 0.1%
440.0408799 1
 
< 0.1%
447.4975 7
< 0.1%
ValueCountFrequency (%)
15115.19 2
 
< 0.1%
15090.07667 6
< 0.1%
14978.33667 7
< 0.1%
14960.25 7
< 0.1%
14958.33667 2
 
< 0.1%
14929.54 7
< 0.1%
14880.38333 7
< 0.1%
14867.81333 7
< 0.1%
14862.28333 3
< 0.1%
14856.48333 7
< 0.1%

Num_Tarjetas_Credito
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9387818
Minimum0
Maximum10
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:01.459352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q36
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.773325
Coefficient of variation (CV)0.35906121
Kurtosis-0.060390999
Mean4.9387818
Median Absolute Deviation (MAD)1
Skewness0.18308625
Sum95600
Variance3.1446816
MonotonicityNot monotonic
2025-04-17T00:53:01.535297image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5 3809
19.7%
4 3725
19.2%
3 3416
17.6%
7 3383
17.5%
6 3164
16.3%
2 538
 
2.8%
1 527
 
2.7%
9 285
 
1.5%
10 271
 
1.4%
8 233
 
1.2%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 527
 
2.7%
2 538
 
2.8%
3 3416
17.6%
4 3725
19.2%
5 3809
19.7%
6 3164
16.3%
7 3383
17.5%
8 233
 
1.2%
9 285
 
1.5%
ValueCountFrequency (%)
10 271
 
1.4%
9 285
 
1.5%
8 233
 
1.2%
7 3383
17.5%
6 3164
16.3%
5 3809
19.7%
4 3725
19.2%
3 3416
17.6%
2 538
 
2.8%
1 527
 
2.7%

Tasa_Interes
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.025624
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:01.619323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median10
Q315
95-th percentile25
Maximum34
Range33
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.9246725
Coefficient of variation (CV)0.62805268
Kurtosis0.90727853
Mean11.025624
Median Absolute Deviation (MAD)4
Skewness0.97471212
Sum213423
Variance47.95109
MonotonicityNot monotonic
2025-04-17T00:53:01.718704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
5 1298
 
6.7%
6 1297
 
6.7%
12 1296
 
6.7%
8 1273
 
6.6%
7 1263
 
6.5%
10 1200
 
6.2%
11 1163
 
6.0%
9 1146
 
5.9%
3 812
 
4.2%
1 748
 
3.9%
Other values (24) 7861
40.6%
ValueCountFrequency (%)
1 748
3.9%
2 710
3.7%
3 812
4.2%
4 727
3.8%
5 1298
6.7%
6 1297
6.7%
7 1263
6.5%
8 1273
6.6%
9 1146
5.9%
10 1200
6.2%
ValueCountFrequency (%)
34 98
0.5%
33 99
0.5%
32 108
0.6%
31 137
0.7%
30 76
0.4%
29 92
0.5%
28 137
0.7%
27 85
0.4%
26 89
0.5%
25 76
0.4%

Retraso_Pago
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.021801
Minimum0
Maximum62
Zeros330
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:01.825292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median14
Q323
95-th percentile34
Maximum62
Range62
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.11402
Coefficient of variation (CV)0.69368104
Kurtosis2.5509176
Mean16.021801
Median Absolute Deviation (MAD)7
Skewness1.3257482
Sum310134
Variance123.52143
MonotonicityNot monotonic
2025-04-17T00:53:01.936092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 862
 
4.5%
14 830
 
4.3%
11 828
 
4.3%
5 819
 
4.2%
13 817
 
4.2%
15 810
 
4.2%
7 810
 
4.2%
8 809
 
4.2%
12 792
 
4.1%
10 787
 
4.1%
Other values (53) 11193
57.8%
ValueCountFrequency (%)
0 330
 
1.7%
1 403
2.1%
2 364
1.9%
3 477
2.5%
4 499
2.6%
5 819
4.2%
6 772
4.0%
7 810
4.2%
8 809
4.2%
9 862
4.5%
ValueCountFrequency (%)
62 35
0.2%
61 33
0.2%
60 45
0.2%
59 23
0.1%
58 33
0.2%
57 36
0.2%
56 24
0.1%
55 38
0.2%
54 25
0.1%
53 29
0.1%

Num_Pagos_Retrasados
Real number (ℝ)

High correlation  Zeros 

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.450586
Minimum0
Maximum25
Zeros519
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:02.034653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median11
Q316
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.8443685
Coefficient of variation (CV)0.51039906
Kurtosis-0.72954847
Mean11.450586
Median Absolute Deviation (MAD)4
Skewness-0.068149551
Sum221649
Variance34.156643
MonotonicityNot monotonic
2025-04-17T00:53:02.124907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10 1386
 
7.2%
12 1338
 
6.9%
9 1267
 
6.5%
8 1237
 
6.4%
11 1122
 
5.8%
19 997
 
5.2%
13 991
 
5.1%
14 978
 
5.1%
16 973
 
5.0%
15 966
 
5.0%
Other values (16) 8102
41.9%
ValueCountFrequency (%)
0 519
2.7%
1 618
3.2%
2 529
2.7%
3 562
2.9%
4 544
2.8%
5 674
3.5%
6 594
3.1%
7 679
3.5%
8 1237
6.4%
9 1267
6.5%
ValueCountFrequency (%)
25 86
 
0.4%
24 135
 
0.7%
23 147
 
0.8%
22 196
 
1.0%
21 264
 
1.4%
20 804
4.2%
19 997
5.2%
18 817
4.2%
17 934
4.8%
16 973
5.0%

Cambio_Limite_Credito
Real number (ℝ)

Distinct2201
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5521827
Minimum0.5
Maximum29.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:02.232344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.3
Q14.5
median8.2
Q311.51
95-th percentile18.2
Maximum29.85
Range29.35
Interquartile range (IQR)7.01

Descriptive statistics

Standard deviation5.0875588
Coefficient of variation (CV)0.59488426
Kurtosis-0.088569548
Mean8.5521827
Median Absolute Deviation (MAD)3.48
Skewness0.5630988
Sum165544.6
Variance25.883255
MonotonicityNot monotonic
2025-04-17T00:53:02.343819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.69 58
 
0.3%
11.5 54
 
0.3%
4.72 48
 
0.2%
11.49 44
 
0.2%
1.51 40
 
0.2%
4.29 39
 
0.2%
5.08 37
 
0.2%
3.66 37
 
0.2%
11.66 36
 
0.2%
9.32 36
 
0.2%
Other values (2191) 18928
97.8%
ValueCountFrequency (%)
0.5 17
0.1%
0.51 15
0.1%
0.52 2
 
< 0.1%
0.52 17
0.1%
0.53 17
0.1%
0.54 13
0.1%
0.55 2
 
< 0.1%
0.55 7
 
< 0.1%
0.56 6
 
< 0.1%
0.57 24
0.1%
ValueCountFrequency (%)
29.85 2
 
< 0.1%
29.2 1
 
< 0.1%
28.45 2
 
< 0.1%
28.04 5
< 0.1%
27.7 6
< 0.1%
27.45 1
 
< 0.1%
27.15 1
 
< 0.1%
26.62 1
 
< 0.1%
26.16 6
< 0.1%
26.12 1
 
< 0.1%

Num_Consultas_Credito
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0858088
Minimum0
Maximum17
Zeros2310
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:02.430137image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2233974
Coefficient of variation (CV)0.78892518
Kurtosis0.56465987
Mean4.0858088
Median Absolute Deviation (MAD)2
Skewness0.92106159
Sum79089
Variance10.390291
MonotonicityNot monotonic
2025-04-17T00:53:02.512135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4 3081
15.9%
3 2604
13.5%
2 2410
12.5%
0 2310
11.9%
1 2232
11.5%
7 1255
6.5%
6 1233
6.4%
5 1231
 
6.4%
8 1037
 
5.4%
9 597
 
3.1%
Other values (8) 1367
7.1%
ValueCountFrequency (%)
0 2310
11.9%
1 2232
11.5%
2 2410
12.5%
3 2604
13.5%
4 3081
15.9%
5 1231
 
6.4%
6 1233
6.4%
7 1255
6.5%
8 1037
 
5.4%
9 597
 
3.1%
ValueCountFrequency (%)
17 17
 
0.1%
16 34
 
0.2%
15 50
 
0.3%
14 73
 
0.4%
13 88
 
0.5%
12 364
 
1.9%
11 367
 
1.9%
10 374
 
1.9%
9 597
3.1%
8 1037
5.4%

Deuda_Pendiente
Real number (ℝ)

High correlation 

Distinct3128
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean904.90999
Minimum0.54
Maximum4921.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:02.614754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile85.31
Q1415.77
median843.36
Q31262.12
95-th percentile2187.636
Maximum4921.57
Range4921.03
Interquartile range (IQR)846.35

Descriptive statistics

Standard deviation643.33122
Coefficient of variation (CV)0.71093394
Kurtosis4.0864261
Mean904.90999
Median Absolute Deviation (MAD)422.47
Skewness1.3975121
Sum17516343
Variance413875.06
MonotonicityNot monotonic
2025-04-17T00:53:02.842835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
544.02 15
 
0.1%
6.41 15
 
0.1%
1217.99 15
 
0.1%
1223 14
 
0.1%
982.81 14
 
0.1%
55.2 14
 
0.1%
69.84 14
 
0.1%
1135.31 14
 
0.1%
904.83 14
 
0.1%
0.95 14
 
0.1%
Other values (3118) 19214
99.3%
ValueCountFrequency (%)
0.54 7
< 0.1%
0.95 14
0.1%
1.2 7
< 0.1%
1.23 6
 
< 0.1%
1.48 7
< 0.1%
2.43 6
 
< 0.1%
4.5 6
 
< 0.1%
5.28 8
< 0.1%
5.67 1
 
< 0.1%
6.41 15
0.1%
ValueCountFrequency (%)
4921.57 3
< 0.1%
4919.89 6
< 0.1%
4821.89 5
< 0.1%
4761.49 7
< 0.1%
4760.65 2
 
< 0.1%
4683.73 1
 
< 0.1%
4579.44 7
< 0.1%
4570.99 3
< 0.1%
4467.44 3
< 0.1%
4460.12 5
< 0.1%

Edad_Historial_Credito
Real number (ℝ)

High correlation 

Distinct261
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301.24952
Minimum144
Maximum404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:02.952682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum144
5-th percentile192
Q1240
median311
Q3360
95-th percentile392
Maximum404
Range260
Interquartile range (IQR)120

Descriptive statistics

Standard deviation66.468396
Coefficient of variation (CV)0.22064233
Kurtosis-1.1462105
Mean301.24952
Median Absolute Deviation (MAD)57
Skewness-0.28746714
Sum5831287
Variance4418.0477
MonotonicityNot monotonic
2025-04-17T00:53:03.066976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
352 140
 
0.7%
383 138
 
0.7%
394 134
 
0.7%
346 134
 
0.7%
347 133
 
0.7%
395 133
 
0.7%
363 132
 
0.7%
374 131
 
0.7%
358 130
 
0.7%
377 130
 
0.7%
Other values (251) 18022
93.1%
ValueCountFrequency (%)
144 4
 
< 0.1%
145 4
 
< 0.1%
146 7
< 0.1%
147 3
 
< 0.1%
148 6
< 0.1%
149 4
 
< 0.1%
150 8
< 0.1%
151 10
0.1%
152 14
0.1%
153 12
0.1%
ValueCountFrequency (%)
404 6
 
< 0.1%
403 8
 
< 0.1%
402 25
 
0.1%
401 44
 
0.2%
400 74
0.4%
399 92
0.5%
398 96
0.5%
397 97
0.5%
396 115
0.6%
395 133
0.7%

Total_Cuota_Mensual
Real number (ℝ)

High correlation 

Distinct3335
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.68614
Minimum4.8656897
Maximum1724
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:03.177788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.8656897
5-th percentile13.783563
Q135.490955
median71.198704
Q3150.12778
95-th percentile315.89704
Maximum1724
Range1719.1343
Interquartile range (IQR)114.63682

Descriptive statistics

Standard deviation129.15786
Coefficient of variation (CV)1.1564359
Kurtosis32.045591
Mean111.68614
Median Absolute Deviation (MAD)44.452003
Skewness4.3481483
Sum2161908.5
Variance16681.753
MonotonicityNot monotonic
2025-04-17T00:53:03.302352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.72074705 8
 
< 0.1%
105.7173158 8
 
< 0.1%
37.78886657 8
 
< 0.1%
134.5816707 8
 
< 0.1%
172.3465509 8
 
< 0.1%
9.742872376 8
 
< 0.1%
391.6293696 8
 
< 0.1%
251.7271912 8
 
< 0.1%
158.3709313 8
 
< 0.1%
49.67208144 8
 
< 0.1%
Other values (3325) 19277
99.6%
ValueCountFrequency (%)
4.865689677 6
< 0.1%
5.262291048 6
< 0.1%
5.351086151 6
< 0.1%
5.463308978 6
< 0.1%
5.629824417 6
< 0.1%
5.76627588 7
< 0.1%
5.994046459 7
< 0.1%
5.994895587 8
< 0.1%
6.047450347 7
< 0.1%
6.412118995 5
< 0.1%
ValueCountFrequency (%)
1724 1
 
< 0.1%
1582.324556 5
< 0.1%
1568.862242 4
< 0.1%
1536.725925 5
< 0.1%
1469 1
 
< 0.1%
1466.973964 1
 
< 0.1%
1435.127574 3
< 0.1%
1320.549063 4
< 0.1%
1278.186251 3
< 0.1%
1245.569803 3
< 0.1%

Inversion_Mensual
Real number (ℝ)

High correlation  Zeros 

Distinct3126
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.017931
Minimum0
Maximum434.19109
Zeros196
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:03.418843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.759102
Q131.161401
median50.06596
Q381.184447
95-th percentile147.47247
Maximum434.19109
Range434.19109
Interquartile range (IQR)50.023047

Descriptive statistics

Standard deviation43.27048
Coefficient of variation (CV)0.69770919
Kurtosis4.0595015
Mean62.017931
Median Absolute Deviation (MAD)22.68104
Skewness1.6134308
Sum1200481.1
Variance1872.3345
MonotonicityNot monotonic
2025-04-17T00:53:03.525679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 196
 
1.0%
79.59342555 8
 
< 0.1%
48.58888904 8
 
< 0.1%
49.25139247 8
 
< 0.1%
81.07835188 8
 
< 0.1%
44.73597007 8
 
< 0.1%
39.58827438 8
 
< 0.1%
26.40883435 8
 
< 0.1%
75.40914763 8
 
< 0.1%
13.29077355 8
 
< 0.1%
Other values (3116) 19089
98.6%
ValueCountFrequency (%)
0 196
1.0%
10.02534446 7
 
< 0.1%
10.05105695 7
 
< 0.1%
10.07193677 7
 
< 0.1%
10.22647426 6
 
< 0.1%
10.28340438 5
 
< 0.1%
10.31583837 7
 
< 0.1%
10.35874106 5
 
< 0.1%
10.41187622 6
 
< 0.1%
10.51830213 7
 
< 0.1%
ValueCountFrequency (%)
434.1910894 6
< 0.1%
297.0646696 6
< 0.1%
295.9554859 3
< 0.1%
270.9726666 7
< 0.1%
262.4833024 5
< 0.1%
251.6624418 7
< 0.1%
251.5353347 4
< 0.1%
248.7162254 6
< 0.1%
246.1208399 7
< 0.1%
244.274694 5
< 0.1%

Saldo_Mensual
Real number (ℝ)

High correlation 

Distinct19102
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean426.56119
Minimum0.088627865
Maximum1183.9307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:03.630263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.088627865
5-th percentile171.83619
Q1285.29913
median364.66323
Q3523.08365
95-th percentile871.81315
Maximum1183.9307
Range1183.8421
Interquartile range (IQR)237.78452

Descriptive statistics

Standard deviation213.15752
Coefficient of variation (CV)0.49971149
Kurtosis0.92819349
Mean426.56119
Median Absolute Deviation (MAD)100.10422
Skewness1.0901359
Sum8256944.9
Variance45436.13
MonotonicityNot monotonic
2025-04-17T00:53:03.744030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1183.930696 7
 
< 0.1%
61.73602238 6
 
< 0.1%
232.9437651 6
 
< 0.1%
232.218042 6
 
< 0.1%
295.2741058 5
 
< 0.1%
281.2076157 5
 
< 0.1%
54.88115252 5
 
< 0.1%
100.9769134 5
 
< 0.1%
319.5728862 5
 
< 0.1%
409.3431502 5
 
< 0.1%
Other values (19092) 19302
99.7%
ValueCountFrequency (%)
0.08862786535 1
 
< 0.1%
0.688298779 4
< 0.1%
0.9081458437 3
< 0.1%
1.325801602 1
 
< 0.1%
1.446074455 1
 
< 0.1%
1.987138164 2
< 0.1%
2.85478859 1
 
< 0.1%
3.357138107 1
 
< 0.1%
3.851841184 2
< 0.1%
4.388830932 1
 
< 0.1%
ValueCountFrequency (%)
1183.930696 7
< 0.1%
1182.121691 1
 
< 0.1%
1182.007057 1
 
< 0.1%
1181.432931 1
 
< 0.1%
1181.293909 1
 
< 0.1%
1178.445869 1
 
< 0.1%
1177.852121 1
 
< 0.1%
1176.35584 1
 
< 0.1%
1173.611002 1
 
< 0.1%
1172.870098 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
16145 
1.0
3212 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16145
83.4%
1.0 3212
 
16.6%

Length

2025-04-17T00:53:03.842610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:03.896919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16145
83.4%
1.0 3212
 
16.6%

Most occurring characters

ValueCountFrequency (%)
0 35502
61.1%
. 19357
33.3%
1 3212
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35502
61.1%
. 19357
33.3%
1 3212
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35502
61.1%
. 19357
33.3%
1 3212
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35502
61.1%
. 19357
33.3%
1 3212
 
5.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
15475 
1.0
3882 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 15475
79.9%
1.0 3882
 
20.1%

Length

2025-04-17T00:53:03.961449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.013010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 15475
79.9%
1.0 3882
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 34832
60.0%
. 19357
33.3%
1 3882
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34832
60.0%
. 19357
33.3%
1 3882
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34832
60.0%
. 19357
33.3%
1 3882
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34832
60.0%
. 19357
33.3%
1 3882
 
6.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
17060 
1.0
2297 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17060
88.1%
1.0 2297
 
11.9%

Length

2025-04-17T00:53:04.075589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.124841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17060
88.1%
1.0 2297
 
11.9%

Most occurring characters

ValueCountFrequency (%)
0 36417
62.7%
. 19357
33.3%
1 2297
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36417
62.7%
. 19357
33.3%
1 2297
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36417
62.7%
. 19357
33.3%
1 2297
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36417
62.7%
. 19357
33.3%
1 2297
 
4.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
17237 
1.0
2120 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 17237
89.0%
1.0 2120
 
11.0%

Length

2025-04-17T00:53:04.189458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.239812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17237
89.0%
1.0 2120
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 36594
63.0%
. 19357
33.3%
1 2120
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 36594
63.0%
. 19357
33.3%
1 2120
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 36594
63.0%
. 19357
33.3%
1 2120
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 36594
63.0%
. 19357
33.3%
1 2120
 
3.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
16524 
1.0
2833 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 16524
85.4%
1.0 2833
 
14.6%

Length

2025-04-17T00:53:04.302306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.354560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 16524
85.4%
1.0 2833
 
14.6%

Most occurring characters

ValueCountFrequency (%)
0 35881
61.8%
. 19357
33.3%
1 2833
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 35881
61.8%
. 19357
33.3%
1 2833
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 35881
61.8%
. 19357
33.3%
1 2833
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 35881
61.8%
. 19357
33.3%
1 2833
 
4.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
14344 
1.0
5013 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 14344
74.1%
1.0 5013
 
25.9%

Length

2025-04-17T00:53:04.423895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.474547image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 14344
74.1%
1.0 5013
 
25.9%

Most occurring characters

ValueCountFrequency (%)
0 33701
58.0%
. 19357
33.3%
1 5013
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 33701
58.0%
. 19357
33.3%
1 5013
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 33701
58.0%
. 19357
33.3%
1 5013
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 33701
58.0%
. 19357
33.3%
1 5013
 
8.6%

Mezcla_Crediticia_Bad
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18206 
1.0
 
1151

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18206
94.1%
1.0 1151
 
5.9%

Length

2025-04-17T00:53:04.541891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.589988image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18206
94.1%
1.0 1151
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 37563
64.7%
. 19357
33.3%
1 1151
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37563
64.7%
. 19357
33.3%
1 1151
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37563
64.7%
. 19357
33.3%
1 1151
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37563
64.7%
. 19357
33.3%
1 1151
 
2.0%

Mezcla_Crediticia_Good
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
11014 
1.0
8343 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 11014
56.9%
1.0 8343
43.1%

Length

2025-04-17T00:53:04.648596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.699951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 11014
56.9%
1.0 8343
43.1%

Most occurring characters

ValueCountFrequency (%)
0 30371
52.3%
. 19357
33.3%
1 8343
 
14.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 30371
52.3%
. 19357
33.3%
1 8343
 
14.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 30371
52.3%
. 19357
33.3%
1 8343
 
14.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 30371
52.3%
. 19357
33.3%
1 8343
 
14.4%

Mezcla_Crediticia_Standard
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
1.0
9863 
0.0
9494 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 9863
51.0%
0.0 9494
49.0%

Length

2025-04-17T00:53:04.762519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.812829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 9863
51.0%
0.0 9494
49.0%

Most occurring characters

ValueCountFrequency (%)
0 28851
49.7%
. 19357
33.3%
1 9863
 
17.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28851
49.7%
. 19357
33.3%
1 9863
 
17.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28851
49.7%
. 19357
33.3%
1 9863
 
17.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28851
49.7%
. 19357
33.3%
1 9863
 
17.0%

Pago_Minimo_No
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
1.0
10180 
0.0
9177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 10180
52.6%
0.0 9177
47.4%

Length

2025-04-17T00:53:04.875416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:04.925723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 10180
52.6%
0.0 9177
47.4%

Most occurring characters

ValueCountFrequency (%)
0 28534
49.1%
. 19357
33.3%
1 10180
 
17.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 28534
49.1%
. 19357
33.3%
1 10180
 
17.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 28534
49.1%
. 19357
33.3%
1 10180
 
17.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 28534
49.1%
. 19357
33.3%
1 10180
 
17.5%

Pago_Minimo_Yes
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
12601 
1.0
6756 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 12601
65.1%
1.0 6756
34.9%

Length

2025-04-17T00:53:04.988527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.039835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 12601
65.1%
1.0 6756
34.9%

Most occurring characters

ValueCountFrequency (%)
0 31958
55.0%
. 19357
33.3%
1 6756
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 31958
55.0%
. 19357
33.3%
1 6756
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 31958
55.0%
. 19357
33.3%
1 6756
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 31958
55.0%
. 19357
33.3%
1 6756
 
11.6%

Ocupacion_Architect
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
17810 
1.0
 
1547

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17810
92.0%
1.0 1547
 
8.0%

Length

2025-04-17T00:53:05.102595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.150874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17810
92.0%
1.0 1547
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 37167
64.0%
. 19357
33.3%
1 1547
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37167
64.0%
. 19357
33.3%
1 1547
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37167
64.0%
. 19357
33.3%
1 1547
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37167
64.0%
. 19357
33.3%
1 1547
 
2.7%

Ocupacion_Developer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18159 
1.0
 
1198

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18159
93.8%
1.0 1198
 
6.2%

Length

2025-04-17T00:53:05.211241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.259525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18159
93.8%
1.0 1198
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 37516
64.6%
. 19357
33.3%
1 1198
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37516
64.6%
. 19357
33.3%
1 1198
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37516
64.6%
. 19357
33.3%
1 1198
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37516
64.6%
. 19357
33.3%
1 1198
 
2.1%

Ocupacion_Doctor
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18041 
1.0
 
1316

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18041
93.2%
1.0 1316
 
6.8%

Length

2025-04-17T00:53:05.319108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.369395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18041
93.2%
1.0 1316
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 37398
64.4%
. 19357
33.3%
1 1316
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37398
64.4%
. 19357
33.3%
1 1316
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37398
64.4%
. 19357
33.3%
1 1316
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37398
64.4%
. 19357
33.3%
1 1316
 
2.3%

Ocupacion_Engineer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18020 
1.0
 
1337

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18020
93.1%
1.0 1337
 
6.9%

Length

2025-04-17T00:53:05.430943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.488439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18020
93.1%
1.0 1337
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 37377
64.4%
. 19357
33.3%
1 1337
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37377
64.4%
. 19357
33.3%
1 1337
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37377
64.4%
. 19357
33.3%
1 1337
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37377
64.4%
. 19357
33.3%
1 1337
 
2.3%

Ocupacion_Entrepreneur
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18059 
1.0
 
1298

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18059
93.3%
1.0 1298
 
6.7%

Length

2025-04-17T00:53:05.550973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.603914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18059
93.3%
1.0 1298
 
6.7%

Most occurring characters

ValueCountFrequency (%)
0 37416
64.4%
. 19357
33.3%
1 1298
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37416
64.4%
. 19357
33.3%
1 1298
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37416
64.4%
. 19357
33.3%
1 1298
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37416
64.4%
. 19357
33.3%
1 1298
 
2.2%

Ocupacion_Journalist
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18202 
1.0
 
1155

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18202
94.0%
1.0 1155
 
6.0%

Length

2025-04-17T00:53:05.663470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.714712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18202
94.0%
1.0 1155
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 37559
64.7%
. 19357
33.3%
1 1155
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37559
64.7%
. 19357
33.3%
1 1155
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37559
64.7%
. 19357
33.3%
1 1155
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37559
64.7%
. 19357
33.3%
1 1155
 
2.0%

Ocupacion_Lawyer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18042 
1.0
 
1315

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18042
93.2%
1.0 1315
 
6.8%

Length

2025-04-17T00:53:05.774276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.822568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18042
93.2%
1.0 1315
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 37399
64.4%
. 19357
33.3%
1 1315
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37399
64.4%
. 19357
33.3%
1 1315
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37399
64.4%
. 19357
33.3%
1 1315
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37399
64.4%
. 19357
33.3%
1 1315
 
2.3%

Ocupacion_Manager
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
17998 
1.0
 
1359

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 17998
93.0%
1.0 1359
 
7.0%

Length

2025-04-17T00:53:05.884121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:05.932181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 17998
93.0%
1.0 1359
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 37355
64.3%
. 19357
33.3%
1 1359
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37355
64.3%
. 19357
33.3%
1 1359
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37355
64.3%
. 19357
33.3%
1 1359
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37355
64.3%
. 19357
33.3%
1 1359
 
2.3%

Ocupacion_Mechanic
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18077 
1.0
 
1280

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 18077
93.4%
1.0 1280
 
6.6%

Length

2025-04-17T00:53:05.992846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.043145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18077
93.4%
1.0 1280
 
6.6%

Most occurring characters

ValueCountFrequency (%)
0 37434
64.5%
. 19357
33.3%
1 1280
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37434
64.5%
. 19357
33.3%
1 1280
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37434
64.5%
. 19357
33.3%
1 1280
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37434
64.5%
. 19357
33.3%
1 1280
 
2.2%

Ocupacion_Media_Manager
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18021 
1.0
 
1336

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18021
93.1%
1.0 1336
 
6.9%

Length

2025-04-17T00:53:06.103676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.151967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18021
93.1%
1.0 1336
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 37378
64.4%
. 19357
33.3%
1 1336
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37378
64.4%
. 19357
33.3%
1 1336
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37378
64.4%
. 19357
33.3%
1 1336
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37378
64.4%
. 19357
33.3%
1 1336
 
2.3%

Ocupacion_Musician
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18123 
1.0
 
1234

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18123
93.6%
1.0 1234
 
6.4%

Length

2025-04-17T00:53:06.213523image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.261819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18123
93.6%
1.0 1234
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 37480
64.5%
. 19357
33.3%
1 1234
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37480
64.5%
. 19357
33.3%
1 1234
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37480
64.5%
. 19357
33.3%
1 1234
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37480
64.5%
. 19357
33.3%
1 1234
 
2.1%

Ocupacion_Scientist
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18036 
1.0
 
1321

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18036
93.2%
1.0 1321
 
6.8%

Length

2025-04-17T00:53:06.321488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.369799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18036
93.2%
1.0 1321
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 37393
64.4%
. 19357
33.3%
1 1321
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37393
64.4%
. 19357
33.3%
1 1321
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37393
64.4%
. 19357
33.3%
1 1321
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37393
64.4%
. 19357
33.3%
1 1321
 
2.3%

Ocupacion_Teacher
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18114 
1.0
 
1243

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18114
93.6%
1.0 1243
 
6.4%

Length

2025-04-17T00:53:06.547785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.600894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18114
93.6%
1.0 1243
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 37471
64.5%
. 19357
33.3%
1 1243
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37471
64.5%
. 19357
33.3%
1 1243
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37471
64.5%
. 19357
33.3%
1 1243
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37471
64.5%
. 19357
33.3%
1 1243
 
2.1%

Ocupacion_Writer
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size983.1 KiB
0.0
18106 
1.0
 
1251

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters58071
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 18106
93.5%
1.0 1251
 
6.5%

Length

2025-04-17T00:53:06.661774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-17T00:53:06.712634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 18106
93.5%
1.0 1251
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 37463
64.5%
. 19357
33.3%
1 1251
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 37463
64.5%
. 19357
33.3%
1 1251
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 37463
64.5%
. 19357
33.3%
1 1251
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 37463
64.5%
. 19357
33.3%
1 1251
 
2.2%

debt_to_income
Real number (ℝ)

High correlation 

Distinct3325
Distinct (%)17.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37005007
Minimum4.9871489 × 10-5
Maximum11.152845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:06.787762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.9871489 × 10-5
5-th percentile0.017496364
Q10.079617583
median0.18293404
Q30.43585623
95-th percentile1.2928915
Maximum11.152845
Range11.152795
Interquartile range (IQR)0.35623864

Descriptive statistics

Standard deviation0.56362105
Coefficient of variation (CV)1.523094
Kurtosis36.795294
Mean0.37005007
Median Absolute Deviation (MAD)0.13195595
Skewness4.633825
Sum7163.0592
Variance0.31766868
MonotonicityNot monotonic
2025-04-17T00:53:06.898663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.236389615 8
 
< 0.1%
0.06901785204 8
 
< 0.1%
0.07846725901 8
 
< 0.1%
1.441540455 8
 
< 0.1%
0.1230158049 8
 
< 0.1%
0.3674622622 8
 
< 0.1%
0.061869603 8
 
< 0.1%
0.04971208486 8
 
< 0.1%
0.01034112313 8
 
< 0.1%
0.03704596491 8
 
< 0.1%
Other values (3315) 19277
99.6%
ValueCountFrequency (%)
4.987148856 × 10-57
< 0.1%
0.0001883884009 1
 
< 0.1%
0.0001953079131 6
< 0.1%
0.0002715908793 7
< 0.1%
0.0003473972946 7
< 0.1%
0.0006114719294 8
< 0.1%
0.0006440133375 6
< 0.1%
0.0007346131246 8
< 0.1%
0.0008134705819 6
< 0.1%
0.0008967503641 4
< 0.1%
ValueCountFrequency (%)
11.15284505 2
 
< 0.1%
6.346431968 7
< 0.1%
6.337295238 7
< 0.1%
6.014723224 5
< 0.1%
4.824395921 4
< 0.1%
4.539369419 7
< 0.1%
4.329946792 3
< 0.1%
4.29738895 6
< 0.1%
4.281695926 2
 
< 0.1%
4.188944068 7
< 0.1%

payment_to_income
Real number (ℝ)

High correlation 

Distinct3402
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02464369
Minimum0.004429483
Maximum1.0653665
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:07.009253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.004429483
5-th percentile0.0061684968
Q10.011924293
median0.02014047
Q30.029539547
95-th percentile0.057058952
Maximum1.0653665
Range1.060937
Interquartile range (IQR)0.017615254

Descriptive statistics

Standard deviation0.023723659
Coefficient of variation (CV)0.96266668
Kurtosis322.7468
Mean0.02464369
Median Absolute Deviation (MAD)0.0087997586
Skewness10.524339
Sum477.02791
Variance0.00056281201
MonotonicityNot monotonic
2025-04-17T00:53:07.123307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.009520057205 8
 
< 0.1%
0.01527157579 8
 
< 0.1%
0.01632610305 8
 
< 0.1%
0.06664265829 8
 
< 0.1%
0.03453949641 8
 
< 0.1%
0.01295216434 8
 
< 0.1%
0.02435600772 8
 
< 0.1%
0.01871944803 8
 
< 0.1%
0.02066664657 8
 
< 0.1%
0.02628499932 8
 
< 0.1%
Other values (3392) 19277
99.6%
ValueCountFrequency (%)
0.004429482961 4
< 0.1%
0.004543692842 1
 
< 0.1%
0.004553168148 7
< 0.1%
0.004732171077 4
< 0.1%
0.004783509326 5
< 0.1%
0.004812806587 6
< 0.1%
0.004819616393 7
< 0.1%
0.004821449807 8
< 0.1%
0.004868627082 6
< 0.1%
0.004873816902 7
< 0.1%
ValueCountFrequency (%)
1.065366475 1
 
< 0.1%
0.8851275282 1
 
< 0.1%
0.5897494401 1
 
< 0.1%
0.588629435 1
 
< 0.1%
0.2130295301 5
< 0.1%
0.19683311 4
< 0.1%
0.183457187 6
< 0.1%
0.1818974756 4
< 0.1%
0.180014243 2
 
< 0.1%
0.1749280965 3
< 0.1%

credit_history_ratio
Real number (ℝ)

High correlation 

Distinct3314
Distinct (%)17.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.314803
Minimum8
Maximum21.944444
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size151.4 KiB
2025-04-17T00:53:07.228890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8.1666667
Q19.0571429
median10.484848
Q312.782609
95-th percentile17.316268
Maximum21.944444
Range13.944444
Interquartile range (IQR)3.7254658

Descriptive statistics

Standard deviation2.9106932
Coefficient of variation (CV)0.25724649
Kurtosis0.93067781
Mean11.314803
Median Absolute Deviation (MAD)1.6792929
Skewness1.1886859
Sum219020.63
Variance8.472135
MonotonicityNot monotonic
2025-04-17T00:53:07.341679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 185
 
1.0%
9 131
 
0.7%
10 119
 
0.6%
11 87
 
0.4%
8.5 80
 
0.4%
9.5 66
 
0.3%
12 65
 
0.3%
10.5 61
 
0.3%
8.333333333 55
 
0.3%
13 50
 
0.3%
Other values (3304) 18458
95.4%
ValueCountFrequency (%)
8 185
1.0%
8.02 3
 
< 0.1%
8.020408163 5
 
< 0.1%
8.020833333 5
 
< 0.1%
8.021276596 5
 
< 0.1%
8.02173913 3
 
< 0.1%
8.022222222 3
 
< 0.1%
8.022727273 1
 
< 0.1%
8.023255814 2
 
< 0.1%
8.023809524 1
 
< 0.1%
ValueCountFrequency (%)
21.94444444 3
 
< 0.1%
21.88888889 4
< 0.1%
21.83333333 4
< 0.1%
21.77777778 8
< 0.1%
21.72222222 5
< 0.1%
21.66666667 6
< 0.1%
21.61111111 8
< 0.1%
21.55555556 7
< 0.1%
21.5 6
< 0.1%
21.44444444 7
< 0.1%

Interactions

2025-04-17T00:52:58.688220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:37.454136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:38.895046image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:40.333776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:41.697459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:43.196458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:44.565365image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:45.904972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:47.381874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:48.846432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:50.216803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:51.729636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:53.175533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:54.491818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:55.998064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:57.319948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:58.776571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:37.540445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:38.982399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:40.414845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:41.784895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:43.279797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:44.648519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:45.989234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:47.469235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:48.927605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:50.307251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:51.816977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:53.255601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:54.588032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:56.079158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:57.403120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:58.869878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:37.628492image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:39.072490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:40.512700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:41.876125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:43.367859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:44.735992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:46.082240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:47.562629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:49.014621image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:50.511461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:51.910253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:53.339668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:54.682822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:56.164235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:57.491480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:58.967022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:37.713481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-17T00:52:53.085202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:54.406732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:55.910725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:57.235544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-17T00:52:58.602074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-17T00:53:07.481278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Cambio_Limite_CreditoComportamiento_de_Pago_High_spent_Large_value_paymentsComportamiento_de_Pago_High_spent_Medium_value_paymentsComportamiento_de_Pago_High_spent_Small_value_paymentsComportamiento_de_Pago_Low_spent_Large_value_paymentsComportamiento_de_Pago_Low_spent_Medium_value_paymentsComportamiento_de_Pago_Low_spent_Small_value_paymentsDeuda_PendienteEdadEdad_Historial_CreditoInversion_MensualMezcla_Crediticia_BadMezcla_Crediticia_GoodMezcla_Crediticia_StandardNum_Consultas_CreditoNum_Pagos_RetrasadosNum_Tarjetas_CreditoOcupacion_ArchitectOcupacion_DeveloperOcupacion_DoctorOcupacion_EngineerOcupacion_EntrepreneurOcupacion_JournalistOcupacion_LawyerOcupacion_ManagerOcupacion_MechanicOcupacion_Media_ManagerOcupacion_MusicianOcupacion_ScientistOcupacion_TeacherOcupacion_WriterPago_Minimo_NoPago_Minimo_YesRetraso_PagoSalario_MensualSaldo_MensualTasa_InteresTotal_Cuota_Mensualcredit_history_ratiodebt_to_incomepayment_to_income
Cambio_Limite_Credito1.0000.0280.0170.0000.0000.0210.0280.072-0.117-0.151-0.0770.1910.3930.3960.2510.1900.0780.0200.0260.0300.0700.0380.0470.0260.0370.0490.0360.0440.0320.0260.0460.4700.4910.141-0.092-0.0540.228-0.041-0.0370.0980.057
Comportamiento_de_Pago_High_spent_Large_value_payments0.0281.0000.2230.1630.1560.1840.2630.0380.0240.0300.1570.0330.0480.0310.0320.0400.0310.0000.0000.0000.0000.0090.0090.0000.0000.0110.0000.0000.0000.0120.0030.0450.0350.0380.2690.4290.0450.1580.0300.0800.000
Comportamiento_de_Pago_High_spent_Medium_value_payments0.0170.2231.0000.1830.1750.2070.2960.0210.0150.0160.0650.0280.0170.0000.0110.0290.0210.0120.0000.0000.0000.0000.0070.0000.0100.0000.0000.0000.0000.0050.0000.0140.0210.0270.1850.2930.0250.0290.0110.0880.000
Comportamiento_de_Pago_High_spent_Small_value_payments0.0000.1630.1831.0000.1280.1520.2170.0110.0120.0000.0190.0000.0000.0000.0000.0140.0000.0070.0000.0000.0030.0000.0080.0000.0000.0060.0050.0030.0050.0060.0040.0000.0000.0000.0670.1270.0080.0160.0140.0290.010
Comportamiento_de_Pago_Low_spent_Large_value_payments0.0000.1560.1750.1281.0000.1450.2070.0000.0000.0000.0260.0000.0050.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0060.0110.0710.1310.0000.0280.0000.0120.000
Comportamiento_de_Pago_Low_spent_Medium_value_payments0.0210.1840.2070.1520.1451.0000.2450.0020.0080.0180.0920.0000.0150.0150.0170.0150.0170.0110.0000.0000.0000.0060.0000.0000.0000.0050.0000.0000.0050.0000.0000.0090.0100.0000.1360.1200.0000.0500.0220.0000.000
Comportamiento_de_Pago_Low_spent_Small_value_payments0.0280.2630.2960.2170.2070.2451.0000.0730.0270.0600.2170.0520.0660.0400.0670.0760.0610.0000.0000.0000.0000.0170.0000.0000.0000.0140.0120.0060.0000.0000.0000.0550.0580.0650.3380.4440.0700.1490.0260.1640.011
Deuda_Pendiente0.0720.0380.0210.0110.0000.0020.0731.000-0.156-0.236-0.0870.6370.2730.0900.2820.1840.1940.0540.0770.0240.0710.0330.0380.0240.0430.0580.0590.0510.0410.0490.0450.3340.3570.227-0.105-0.1020.2680.013-0.1240.7440.150
Edad-0.1170.0240.0150.0120.0000.0080.027-0.1561.0000.5290.0680.2230.1260.050-0.221-0.124-0.0890.0320.0590.0690.0630.0420.0310.0550.0520.0380.0430.0370.0570.0650.0560.1690.184-0.1080.0850.098-0.152-0.029-0.523-0.147-0.146
Edad_Historial_Credito-0.1510.0300.0160.0000.0000.0180.060-0.2360.5291.0000.1110.4710.2810.105-0.307-0.213-0.1610.0370.0150.0290.0490.0470.0560.0490.0830.0780.0570.0450.0360.0920.0140.3290.358-0.1830.1230.137-0.268-0.0480.347-0.216-0.232
Inversion_Mensual-0.0770.1570.0650.0190.0260.0920.217-0.0870.0680.1111.0000.1040.1910.141-0.119-0.174-0.0910.0350.0420.0690.0430.0650.0470.0290.0180.0620.0510.0740.0350.0300.0250.1480.160-0.1150.8380.454-0.1520.6210.047-0.582-0.089
Mezcla_Crediticia_Bad0.1910.0330.0280.0000.0000.0000.0520.6370.2230.4710.1041.0000.2190.2560.5080.5770.5020.0230.0290.0000.0000.0300.0160.0000.0190.0000.0130.0100.0250.0290.0150.2650.2830.6830.2100.1730.6390.0370.1590.3850.000
Mezcla_Crediticia_Good0.3930.0480.0170.0000.0050.0150.0660.2730.1260.2810.1910.2191.0000.8870.3700.6720.3310.0280.0000.0140.0000.0310.0000.0030.0000.0310.0100.0120.0000.0060.0390.6060.6370.4720.2890.1800.6290.1030.1220.2020.016
Mezcla_Crediticia_Standard0.3960.0310.0000.0000.0050.0150.0400.0900.0500.1050.1410.2560.8871.0000.1860.5890.2610.0390.0190.0130.0000.0150.0080.0080.0040.0330.0180.0030.0150.0230.0470.4750.4970.3970.2330.1210.5240.1130.0520.0440.012
Num_Consultas_Credito0.2510.0320.0110.0000.0000.0170.0670.282-0.221-0.307-0.1190.5080.3700.1861.0000.2900.2120.0470.0760.0230.0560.0480.0560.0510.0380.0260.0360.0410.0420.0520.0560.4410.4670.283-0.148-0.1290.3570.016-0.1150.2660.210
Num_Pagos_Retrasados0.1900.0400.0290.0140.0000.0150.0760.184-0.124-0.213-0.1740.5770.6720.5890.2901.0000.2240.0340.0220.0000.0400.0170.0590.0150.0370.0450.0420.0260.0370.0170.0390.4210.4460.441-0.213-0.1560.453-0.061-0.1070.2470.164
Num_Tarjetas_Credito0.0780.0310.0210.0000.0000.0170.0610.194-0.089-0.161-0.0910.5020.3310.2610.2120.2241.0000.0450.0390.0490.0470.0580.0440.0230.0540.0370.0380.0530.0350.0630.0670.2700.2840.239-0.112-0.1010.2210.002-0.0800.1910.145
Ocupacion_Architect0.0200.0000.0120.0070.0000.0110.0000.0540.0320.0370.0350.0230.0280.0390.0470.0340.0451.0000.0750.0790.0800.0780.0730.0790.0800.0780.0800.0760.0790.0760.0770.0000.0060.0430.0630.0160.0340.0190.0390.0390.003
Ocupacion_Developer0.0260.0000.0000.0000.0000.0000.0000.0770.0590.0150.0420.0290.0000.0190.0760.0220.0390.0751.0000.0690.0690.0680.0640.0690.0700.0680.0690.0660.0690.0660.0670.0000.0000.0680.0780.0180.0590.0490.0410.0510.030
Ocupacion_Doctor0.0300.0000.0000.0000.0000.0000.0000.0240.0690.0290.0690.0000.0140.0130.0230.0000.0490.0790.0691.0000.0730.0720.0670.0720.0730.0710.0730.0700.0720.0700.0700.0000.0070.0270.0190.0000.0520.0330.0600.0250.000
Ocupacion_Engineer0.0700.0000.0000.0030.0000.0000.0000.0710.0630.0490.0430.0000.0000.0000.0560.0400.0470.0800.0690.0731.0000.0720.0680.0730.0740.0720.0730.0700.0730.0710.0710.0110.0130.0340.0560.0240.0520.0530.0460.0270.000
Ocupacion_Entrepreneur0.0380.0090.0000.0000.0000.0060.0170.0330.0420.0470.0650.0300.0310.0150.0480.0170.0580.0780.0680.0720.0721.0000.0670.0720.0730.0710.0720.0690.0720.0690.0700.0250.0270.0320.0570.0420.0550.0570.0350.0640.024
Ocupacion_Journalist0.0470.0090.0070.0080.0000.0000.0000.0380.0310.0560.0470.0160.0000.0080.0560.0590.0440.0730.0640.0670.0680.0671.0000.0670.0680.0660.0680.0650.0670.0650.0650.0130.0210.0430.0420.0210.0610.0320.0410.0180.018
Ocupacion_Lawyer0.0260.0000.0000.0000.0000.0000.0000.0240.0550.0490.0290.0000.0030.0080.0510.0150.0230.0790.0690.0720.0730.0720.0671.0000.0730.0710.0730.0700.0720.0700.0700.0210.0110.0600.0580.0000.0350.0090.0470.0480.000
Ocupacion_Manager0.0370.0000.0100.0000.0000.0000.0000.0430.0520.0830.0180.0190.0000.0040.0380.0370.0540.0800.0700.0730.0740.0730.0680.0731.0000.0720.0740.0710.0740.0710.0710.0190.0190.0310.0540.0200.0550.0380.0560.0450.010
Ocupacion_Mechanic0.0490.0110.0000.0060.0000.0050.0140.0580.0380.0780.0620.0000.0310.0330.0260.0450.0370.0780.0680.0710.0720.0710.0660.0710.0721.0000.0720.0690.0710.0690.0690.0120.0280.0360.0510.0220.0490.0180.0540.0280.000
Ocupacion_Media_Manager0.0360.0000.0000.0050.0000.0000.0120.0590.0430.0570.0510.0130.0100.0180.0360.0420.0380.0800.0690.0730.0730.0720.0680.0730.0740.0721.0000.0700.0730.0710.0710.0310.0240.0290.0490.0000.0610.0300.0410.0420.016
Ocupacion_Musician0.0440.0000.0000.0030.0000.0000.0060.0510.0370.0450.0740.0100.0120.0030.0410.0260.0530.0760.0660.0700.0700.0690.0650.0700.0710.0690.0701.0000.0700.0680.0680.0150.0110.0260.0370.0130.0340.0090.0420.0280.000
Ocupacion_Scientist0.0320.0000.0000.0050.0000.0050.0000.0410.0570.0360.0350.0250.0000.0150.0420.0370.0350.0790.0690.0720.0730.0720.0670.0720.0740.0710.0730.0701.0000.0700.0700.0000.0080.0550.0480.0180.0410.0420.0510.0280.028
Ocupacion_Teacher0.0260.0120.0050.0060.0050.0000.0000.0490.0650.0920.0300.0290.0060.0230.0520.0170.0630.0760.0660.0700.0710.0690.0650.0700.0710.0690.0710.0680.0701.0000.0680.0000.0000.0450.0390.0260.0350.0380.0330.0410.000
Ocupacion_Writer0.0460.0030.0000.0040.0000.0000.0000.0450.0560.0140.0250.0150.0390.0470.0560.0390.0670.0770.0670.0700.0710.0700.0650.0700.0710.0690.0710.0680.0700.0681.0000.0340.0250.0470.0400.0220.0550.0370.0380.0170.035
Pago_Minimo_No0.4700.0450.0140.0000.0000.0090.0550.3340.1690.3290.1480.2650.6060.4750.4410.4210.2700.0000.0000.0000.0110.0250.0130.0210.0190.0120.0310.0150.0000.0000.0341.0000.7710.3530.2190.1570.4750.0600.1230.2270.000
Pago_Minimo_Yes0.4910.0350.0210.0000.0060.0100.0580.3570.1840.3580.1600.2830.6370.4970.4670.4460.2840.0060.0000.0070.0130.0270.0210.0110.0190.0280.0240.0110.0080.0000.0250.7711.0000.3710.2280.1630.4980.0580.1350.2460.009
Retraso_Pago0.1410.0380.0270.0000.0110.0000.0650.227-0.108-0.183-0.1150.6830.4720.3970.2830.4410.2390.0430.0680.0270.0340.0320.0430.0600.0310.0360.0290.0260.0550.0450.0470.3530.3711.000-0.161-0.1290.375-0.022-0.0890.2460.161
Salario_Mensual-0.0920.2690.1850.0670.0710.1360.338-0.1050.0850.1230.8380.2100.2890.233-0.148-0.213-0.1120.0630.0780.0190.0560.0570.0420.0580.0540.0510.0490.0370.0480.0390.0400.2190.228-0.1611.0000.573-0.1850.7330.043-0.692-0.113
Saldo_Mensual-0.0540.4290.2930.1270.1310.1200.444-0.1020.0980.1370.4540.1730.1800.121-0.129-0.156-0.1010.0160.0180.0000.0240.0420.0210.0000.0200.0220.0000.0130.0180.0260.0220.1570.163-0.1290.5731.000-0.1510.2870.046-0.424-0.262
Tasa_Interes0.2280.0450.0250.0080.0000.0000.0700.268-0.152-0.268-0.1520.6390.6290.5240.3570.4530.2210.0340.0590.0520.0520.0550.0610.0350.0550.0490.0610.0340.0410.0350.0550.4750.4980.375-0.185-0.1511.000-0.023-0.1370.2770.196
Total_Cuota_Mensual-0.0410.1580.0290.0160.0280.0500.1490.013-0.029-0.0480.6210.0370.1030.1130.016-0.0610.0020.0190.0490.0330.0530.0570.0320.0090.0380.0180.0300.0090.0420.0380.0370.0600.058-0.0220.7330.287-0.0231.000-0.025-0.4520.562
credit_history_ratio-0.0370.0300.0110.0140.0000.0220.026-0.124-0.5230.3470.0470.1590.1220.052-0.115-0.107-0.0800.0390.0410.0600.0460.0350.0410.0470.0560.0540.0410.0420.0510.0330.0380.1230.135-0.0890.0430.046-0.137-0.0251.000-0.100-0.100
debt_to_income0.0980.0800.0880.0290.0120.0000.1640.744-0.147-0.216-0.5820.3850.2020.0440.2660.2470.1910.0390.0510.0250.0270.0640.0180.0480.0450.0280.0420.0280.0280.0410.0170.2270.2460.246-0.692-0.4240.277-0.452-0.1001.0000.158
payment_to_income0.0570.0000.0000.0100.0000.0000.0110.150-0.146-0.232-0.0890.0000.0160.0120.2100.1640.1450.0030.0300.0000.0000.0240.0180.0000.0100.0000.0160.0000.0280.0000.0350.0000.0090.161-0.113-0.2620.1960.562-0.1000.1581.000

Missing values

2025-04-17T00:53:00.342828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-17T00:53:00.711439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

EdadSalario_MensualNum_Tarjetas_CreditoTasa_InteresRetraso_PagoNum_Pagos_RetrasadosCambio_Limite_CreditoNum_Consultas_CreditoDeuda_PendienteEdad_Historial_CreditoTotal_Cuota_MensualInversion_MensualSaldo_MensualComportamiento_de_Pago_High_spent_Large_value_paymentsComportamiento_de_Pago_High_spent_Medium_value_paymentsComportamiento_de_Pago_High_spent_Small_value_paymentsComportamiento_de_Pago_Low_spent_Large_value_paymentsComportamiento_de_Pago_Low_spent_Medium_value_paymentsComportamiento_de_Pago_Low_spent_Small_value_paymentsMezcla_Crediticia_BadMezcla_Crediticia_GoodMezcla_Crediticia_StandardPago_Minimo_NoPago_Minimo_YesOcupacion_ArchitectOcupacion_DeveloperOcupacion_DoctorOcupacion_EngineerOcupacion_EntrepreneurOcupacion_JournalistOcupacion_LawyerOcupacion_ManagerOcupacion_MechanicOcupacion_Media_ManagerOcupacion_MusicianOcupacion_ScientistOcupacion_TeacherOcupacion_Writerdebt_to_incomepayment_to_incomecredit_history_ratio
038.04896.5566675.07.029.016.07.374.01419.99362.0131.87148290.929602406.7620860.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.2899980.0269319.526316
118.01003.8845836.08.08.09.011.261.045.37222.014.72466220.894529270.1143310.00.00.00.00.01.00.01.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0451940.01466812.333333
219.01309.9104176.018.07.08.018.316.01313.30238.050.09175638.299870210.6202940.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.0025880.03824112.526316
333.02579.8050001.06.06.05.02.420.0301.38378.035.00184335.365135363.4368010.00.00.01.00.00.00.01.00.01.00.00.00.00.01.00.00.00.00.00.00.00.00.00.00.00.1168230.01356811.454545
420.02209.8633336.024.019.017.023.9814.02059.36176.0112.47359334.885490232.9844050.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.9318950.0508968.800000
527.06882.0466675.011.06.016.016.075.01210.87244.074.80770186.033646695.4292900.00.01.00.00.00.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.00.00.00.00.00.1759460.0108709.037037
643.02668.3416673.010.06.05.04.821.0681.09386.019.18656148.889857440.2756870.00.00.01.00.00.00.00.01.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.2552480.0071908.976744
744.04901.2350004.013.021.010.04.082.0636.65365.041.63362674.114120561.7095220.00.00.00.00.01.00.00.01.01.00.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.1298960.0084958.295455
827.02689.6525003.010.019.013.011.691.0905.89222.022.79539658.818110418.7443270.00.00.00.00.01.00.00.01.00.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.3368060.0084758.222222
927.03150.8016676.02.018.019.011.406.0557.48363.057.17550731.935048419.3278150.01.00.00.00.00.00.00.01.01.00.00.00.00.00.00.01.00.00.00.00.00.00.00.00.00.1769330.01814613.444444
EdadSalario_MensualNum_Tarjetas_CreditoTasa_InteresRetraso_PagoNum_Pagos_RetrasadosCambio_Limite_CreditoNum_Consultas_CreditoDeuda_PendienteEdad_Historial_CreditoTotal_Cuota_MensualInversion_MensualSaldo_MensualComportamiento_de_Pago_High_spent_Large_value_paymentsComportamiento_de_Pago_High_spent_Medium_value_paymentsComportamiento_de_Pago_High_spent_Small_value_paymentsComportamiento_de_Pago_Low_spent_Large_value_paymentsComportamiento_de_Pago_Low_spent_Medium_value_paymentsComportamiento_de_Pago_Low_spent_Small_value_paymentsMezcla_Crediticia_BadMezcla_Crediticia_GoodMezcla_Crediticia_StandardPago_Minimo_NoPago_Minimo_YesOcupacion_ArchitectOcupacion_DeveloperOcupacion_DoctorOcupacion_EngineerOcupacion_EntrepreneurOcupacion_JournalistOcupacion_LawyerOcupacion_ManagerOcupacion_MechanicOcupacion_Media_ManagerOcupacion_MusicianOcupacion_ScientistOcupacion_TeacherOcupacion_Writerdebt_to_incomepayment_to_incomecredit_history_ratio
1934725.014835.9733333.04.05.00.06.670.0137.20230.0328.238449137.422517919.8919630.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.00.00.00.00.00.00.00.0092480.0221249.200000
1934827.014302.1733335.06.019.02.04.702.0700.71338.098.712097153.703305295.2741061.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.00.0489930.00690212.518519
1934919.01983.6450004.010.05.018.017.457.0859.23335.017.60356030.121641334.8063960.01.00.00.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.00.4331570.00887417.631579
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